A Haystack pipeline can be perfectly wired and still unsafe. The retriever returns documents. Every component did its job. But if untrusted text moved through the pipeline as ordinary context, the trust boundary was lost. That is the problem this post is about. Not bad Python. A valid component connection only says: this value fits the next component It does not say: this value is safe to influen
The previous three posts covered how events flow from the SDK to the UI, how the timeline renders, and how tool cards visualize. This final post looks at SwiftWork's infrastructure — how data is stored, how state is restored, how Markdown is rendered, how code is highlighted, and how API keys are managed. These components are independent, but all essential to making the app usable. SwiftWork uses
Across the previous seven articles plus a bonus chapter, we thoroughly explored the inner workings of Open Agent SDK — Agent Loop, the tool system, MCP integration, multi-Agent collaboration, conversation persistence, and multi-LLM support. The bonus chapter even embedded the SDK into a macOS native app, Motive, and ran it live. But Motive was just a backend-swap experiment. The real question is:
Comparison: Haystack 2.0 vs. RAGatouille 0.3 for Building High-Accuracy RAG Pipelines for Developer Docs Retrieval-Augmented Generation (RAG) has become the standard for building LLM-powered tools that answer questions using private or domain-specific data. For developer documentation (dev docs) — which includes technical jargon, versioned APIs, code snippets, and structured reference material —